library(dplyr)
library(ggplot2)
library(scales)
library(tidyr)
Census_Data_1 <- read.csv("2017 Census Data.csv")
dim(Census_Data_1)[1] 74001 37Anthony Fitch
Revealjs Presentation
Anthony Fitch
Your Course Number, Cal Poly Pomona
2025-05-05
Data Wrangling 2017 Census Data
Column Names
[1] “TractId” “State” “County” “TotalPop” [5] “Men” “Women” “Hispanic” “White” [9] “Black” “Native” “Asian” “Pacific” [13] “VotingAgeCitizen” “Income” “IncomeErr” “IncomePerCap” [17] “IncomePerCapErr” “Poverty” “ChildPoverty” “Professional” [21] “Service” “Office” “Construction” “Production” [25] “Drive” “Carpool” “Transit” “Walk” [29] “OtherTransp” “WorkAtHome” “MeanCommute” “Employed” [33] “PrivateWork” “PublicWork” “SelfEmployed” “FamilyWork” [37] “Unemployment”
Top 20 States: Men Vs. Women Population
#unique(Census_Data_1$State)
# 1. Prepare the data
top_states_long <- Census_Data_1 %>%
group_by(State) %>%
summarise(
Total_Men = sum(Men, na.rm = TRUE),
Total_Women = sum(Women, na.rm = TRUE)
) %>%
mutate(TotalPop = Total_Men + Total_Women) %>%
arrange(desc(TotalPop)) %>%
slice_head(n = 20) %>%
pivot_longer(cols = c(Total_Men, Total_Women),
names_to = "Gender",
values_to = "Population") %>%
group_by(State) %>%
mutate(Percent = Population / sum(Population) * 100)
# 2. Plot
ggplot(top_states_long, aes(x = reorder(State, TotalPop), y = Population, fill = Gender)) +
geom_bar(stat = "identity") +
geom_text(
aes(label = paste0(round(Percent, 1), "%")),
position = position_stack(vjust = 0.5),
color = "white",
size = 3
) +
coord_flip() +
scale_y_continuous(labels = comma) +
scale_fill_manual(values = c("Total_Men" = "red", "Total_Women" = "purple")) +
labs(
title = "Top 20 States by Population (Men vs Women Stacked with %)",
x = "State",
y = "Population",
fill = "Gender"
) +
theme_minimal()
Wrangling US Education Data
[1] 61218 69 [1] "Label..Grouping."
[2] "AGE.BY.EDUCATIONAL.ATTAINMENT"
[3] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.18.to.24.years"
[4] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.18.to.24.years..Less.than.high.school.graduate"
[5] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.18.to.24.years..High.school.graduate..includes.equivalency."
[6] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.18.to.24.years..Some.college.or.associate.s.degree"
[7] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.18.to.24.years..Bachelor.s.degree.or.higher"
[8] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over"
[9] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..Less.than.9th.grade"
[10] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..9th.to.12th.grade..no.diploma"
[11] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..High.school.graduate..includes.equivalency."
[12] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..Some.college..no.degree"
[13] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..Associate.s.degree"
[14] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..Bachelor.s.degree"
[15] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..Graduate.or.professional.degree"
[16] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..High.school.graduate.or.higher"
[17] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.years.and.over..Bachelor.s.degree.or.higher"
[18] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.to.34.years"
[19] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.to.34.years..High.school.graduate.or.higher"
[20] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.25.to.34.years..Bachelor.s.degree.or.higher"
[21] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.35.to.44.years"
[22] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.35.to.44.years..High.school.graduate.or.higher"
[23] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.35.to.44.years..Bachelor.s.degree.or.higher"
[24] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.45.to.64.years"
[25] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.45.to.64.years..High.school.graduate.or.higher"
[26] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.45.to.64.years..Bachelor.s.degree.or.higher"
[27] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.65.years.and.over"
[28] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.65.years.and.over..High.school.graduate.or.higher"
[29] "AGE.BY.EDUCATIONAL.ATTAINMENT..Population.65.years.and.over..Bachelor.s.degree.or.higher"
[30] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT"
[31] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..White.alone"
[32] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..White.alone..High.school.graduate.or.higher"
[33] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..White.alone..Bachelor.s.degree.or.higher"
[34] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..White.alone..not.Hispanic.or.Latino"
[35] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..White.alone..not.Hispanic.or.Latino..High.school.graduate.or.higher"
[36] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..White.alone..not.Hispanic.or.Latino..Bachelor.s.degree.or.higher"
[37] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Black.alone"
[38] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Black.alone..High.school.graduate.or.higher"
[39] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Black.alone..Bachelor.s.degree.or.higher"
[40] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..American.Indian.or.Alaska.Native.alone"
[41] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..American.Indian.or.Alaska.Native.alone..High.school.graduate.or.higher"
[42] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..American.Indian.or.Alaska.Native.alone..Bachelor.s.degree.or.higher"
[43] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Asian.alone"
[44] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Asian.alone..High.school.graduate.or.higher"
[45] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Asian.alone..Bachelor.s.degree.or.higher"
[46] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Native.Hawaiian.and.Other.Pacific.Islander.alone"
[47] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Native.Hawaiian.and.Other.Pacific.Islander.alone..High.school.graduate.or.higher"
[48] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Native.Hawaiian.and.Other.Pacific.Islander.alone..Bachelor.s.degree.or.higher"
[49] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Some.other.race.alone"
[50] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Some.other.race.alone..High.school.graduate.or.higher"
[51] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Some.other.race.alone..Bachelor.s.degree.or.higher"
[52] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Two.or.more.races"
[53] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Two.or.more.races..High.school.graduate.or.higher"
[54] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Two.or.more.races..Bachelor.s.degree.or.higher"
[55] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Hispanic.or.Latino.Origin"
[56] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Hispanic.or.Latino.Origin..High.school.graduate.or.higher"
[57] "RACE.AND.HISPANIC.OR.LATINO.ORIGIN.BY.EDUCATIONAL.ATTAINMENT..Hispanic.or.Latino.Origin..Bachelor.s.degree.or.higher"
[58] "POVERTY.RATE.FOR.THE.POPULATION.25.YEARS.AND.OVER.FOR.WHOM.POVERTY.STATUS.IS.DETERMINED.BY.EDUCATIONAL.ATTAINMENT.LEVEL"
[59] "POVERTY.RATE.FOR.THE.POPULATION.25.YEARS.AND.OVER.FOR.WHOM.POVERTY.STATUS.IS.DETERMINED.BY.EDUCATIONAL.ATTAINMENT.LEVEL..Less.than.high.school.graduate"
[60] "POVERTY.RATE.FOR.THE.POPULATION.25.YEARS.AND.OVER.FOR.WHOM.POVERTY.STATUS.IS.DETERMINED.BY.EDUCATIONAL.ATTAINMENT.LEVEL..High.school.graduate..includes.equivalency."
[61] "POVERTY.RATE.FOR.THE.POPULATION.25.YEARS.AND.OVER.FOR.WHOM.POVERTY.STATUS.IS.DETERMINED.BY.EDUCATIONAL.ATTAINMENT.LEVEL..Some.college.or.associate.s.degree"
[62] "POVERTY.RATE.FOR.THE.POPULATION.25.YEARS.AND.OVER.FOR.WHOM.POVERTY.STATUS.IS.DETERMINED.BY.EDUCATIONAL.ATTAINMENT.LEVEL..Bachelor.s.degree.or.higher"
[63] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS."
[64] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS...Population.25.years.and.over.with.earnings"
[65] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS...Population.25.years.and.over.with.earnings..Less.than.high.school.graduate"
[66] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS...Population.25.years.and.over.with.earnings..High.school.graduate..includes.equivalency."
[67] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS...Population.25.years.and.over.with.earnings..Some.college.or.associate.s.degree"
[68] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS...Population.25.years.and.over.with.earnings..Bachelor.s.degree"
[69] "MEDIAN.EARNINGS.IN.THE.PAST.12.MONTHS..IN.2023.INFLATION.ADJUSTED.DOLLARS...Population.25.years.and.over.with.earnings..Graduate.or.professional.degree" library(dplyr)
library(tidyr)
# Step 1: Create a new column called County
US_Education <- US_Education %>%
mutate(County = ifelse(grepl("County", Label..Grouping.), Label..Grouping., NA)) %>%
fill(County)
# Step 2 (optional): If you want, you can also clean the County name (remove extra stuff if needed)
# For example, remove extra text like "County:" if it's messy
# US_Education <- US_Education %>%
# mutate(County = str_remove(County, "County: ")) # optional cleaning
US_Education <- US_Education %>%
relocate(County, .before = 1)
print(US_Education)